#This repo is now deprecated. Please use the following links instead:#
- Github repo: https://github.com/medford-group/training-materials
- Jupyter book: https://medford-group.github.io/training-materials/docs/VIP_Info.html
This is the Github repository for course materials pertaining to the "Big Data and Quantum Mechanics" (BDQM) section of the "Vertically Integrated Projects" (VIP) course. Details on course structure and grading are available in the syllabus, and lectures, project descriptions, and other scripts are also available.
All assignments should be submitted via Canvas, and all students should join the Slack channel for efficient communication.
The following papers and resources are useful if you would like to get a head-start on the training semester, or if you are looking for more fundamental details of the topics covered in the course:
- A relatively short "perspective review" to get some quick theoretical background: https://aip.scitation.org/doi/10.1063/1.4704546
- A comprehensive textbook with a lot of practical details: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470447710
- A free online "textbook" with even more practical details and examples: http://kitchingroup.cheme.cmu.edu/dft-book/dft.html
- A "tutorial review" explaining the basic idea behind neural network force fields for molecular dynamics: https://onlinelibrary.wiley.com/doi/full/10.1002/qua.24890
- An overview of the AMP software package with a more general perspective on implementing machine-learned force fields: https://www.sciencedirect.com/science/article/abs/pii/S0010465516301266
- Details and examples of the "Gaussian Multipole" (GMP) features recently developed by our group: https://arxiv.org/abs/2102.02390